We study U.S. Treasury yield curve forecasting under distributional uncertainty and recast forecasting as an operations research and managerial decision problem. Rather than minimizing average forecast error, the forecaster selects a decision rule that minimizes worst case expected loss over an ambiguity set of forecast error distributions. To this end, we propose a distributionally robust ensemble forecasting framework that integrates parametric factor models with high dimensional nonparametric machine learning models through adaptive forecast combinations. The framework consists of three machine learning components. First, a rolling window Factor Augmented Dynamic Nelson Siegel model captures level, slope, and curvature dynamics using principal components extracted from economic indicators. Second, Random Forest models capture nonlinear interactions among macro financial drivers and lagged Treasury yields. Third, distributionally robust forecast combination schemes aggregate heterogeneous forecasts under moment uncertainty, penalizing downside tail risk via expected shortfall and stabilizing second moment estimation through ridge regularized covariance matrices. The severity of the worst case criterion is adjustable, allowing the forecaster to regulate the trade off between robustness and statistical efficiency. Using monthly data, we evaluate out of sample forecasts across maturities and horizons from one to twelve months ahead. Adaptive combinations deliver superior performance at short horizons, while Random Forest forecasts dominate at longer horizons. Extensions to global sovereign bond yields confirm the stability and generalizability of the proposed framework.

Right now, most big AI labs have a team figuring out ways that rogue AIs might escape supervision, or secretly collude with each other against humans. But there's a more mundane way we could lose control of civilisation: we might simply become obsolete. This wouldn't require any hidden plots – if AI and robotics keep improving, it's what happens by default. Well, AI developers are firmly on track to build better replacements for humans in almost every role we play: not just economically as workers and decision-makers, but culturally as artists and creators, and even socially as friends and romantic companions. What place will humans have when AI can do everything we do, only better?

Short text classification is a crucial and challenging aspect of Natural Language Processing. For this reason, there are numerous highly specialized short text classifiers. However, in recent short text research, State of the Art (SOTA) methods for traditional text classification, particularly the pure use of Transformers, have been unexploited. In this work, we examine the performance of a variety of short text classifiers as well as the top performing traditional text classifier. We further investigate the effects on two new real-world short text datasets in an effort to address the issue of becoming overly dependent on benchmark datasets with a limited number of characteristics. Our experiments unambiguously demonstrate that Transformers achieve SOTA accuracy on short text classification tasks, raising the question of whether specialized short text techniques are necessary.

Since the launch of OpenAI's ChatGPT, the chatbot had taken the world by storm. No wonder ChatGPT has made its way to the FinTech Industry too. OpenAI's ChatGPT is revolutionizing FinTech Industry and for all the right reasons. The usage of chatbots in the financial sector to enhance customer service and assistance is transforming the way we interact with technology. Compliance is a major issue in the heavily regulated financial sector.

The size of an economy is measured by GDP (Gross Domestic Product). GDP is calculated by adding up the value of all goods and services produced within a country's borders in a given year. This includes the value of goods and services produced by both the government and the private sector. GDP is typically measured in monetary terms, using current market prices for goods and services. GDP is used as a measure of the size and strength of an economy, as well as its overall level of economic activity.

We may think of Artificial Intelligence as some obscure, futuristic concept of the Fourth Industrial Revolution that is associated with scenes from "out-there" sci-fi movies like Star Wars and Terminator. But AI is already here and being used in a wide variety of technological developments that make our daily responsibilities more efficient and convenient. In fact, scientists believe we are already'cyborgs' because we already have a'digital self', which lives into perpetuity. Artificial intelligence (AI) is intelligence demonstrated by machines and contrasts with natural intelligence displayed by humans. Towards Data Science says "Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that we would normally think would rely on the intelligence of a human."

The future is coming, and it's coming fast. Before we even know what the next significant invention will be, our world could be overturned by something else entirely. But one thing we can count on is that technology will continue to grow and change, including digital investments. Our lives revolve around technology and the advancements it allows. We live through our computers, phones, tablets, and televisions.

Regarding the impact of robots, there is a major debate among people about whether their effect would be positive or negative. The use of robots is growing around us. The following article aims to list the major effects robots will have on human life. Robots generally complete work that is monotonous or strenuous for human beings. The aim of making robots is not to replace humans, but it is to free them from the tedium of performing most of the basic and low-level tasks.

Automation in artificial intelligence has an extensive effect on the economy. Industrialists and giant companies all over the world are further adapting to the idea of automation in artificial intelligence. In India, technological progress, is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. An increment in labor productivity generally translates into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services.

Industrialists and giant companies all over the world are further adapting to the idea of automation in artificial intelligence. In India, technological progress, is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. An increment in labor productivity generally translates into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services. AI should be welcomed for its potential economic benefits.